Category Archives: big data

How big data makes Google and Facebook the most popular digital advertising platforms?

Google and Facebook are the top two advertisement platforms and leaders of the digital market landscape.

Moreover, both platforms offer the most detailed demographic and behavior targeting thanks to the amount of big data they have about their users, allowing them to convey very specific and accurate commercial messages. But let’s define first each Ad platform:

What is Google Ads?

Google is the world’s most popular and largest PPC advertising platform. Paid ads, also known as pay-per-click (PPC) ads, appear at the top of Google search results. They are labeled with the word “ad” to designate it as paid content.

The main Google Ads networks are:

Google Display Network
Google Play
Google Search Network
YouTube
Google Maps

What are Facebook ads?

Facebook is the largest social media platform for paid advertisement. Usually, Ads in this platform are labeled with the word “sponsored” to indicate the content is paid. The following are the networks where Facebook ads appear:

Instagram
Facebook
Messenger
Audience Network
Marketplace
News feed
Stories
Instant Articles
Suggested video
Right column
Instant Articles

What kind of big data Google and Facebook collect from their users?

Both advertisement platforms store large amounts of demographic data from their users such as their gender, age, hobbies, career, interests, relationship status and income.

Also, they collect technical information such as users’ location, when and how long they have been in a certain place, installed applications, when such applications are used, downloaded programs in users’ computers or mobile phones, users’ contacts, call history, access to webcams and microphones, emails, calendars, messages sent and received, downloaded files, played video games, shared photos and videos, comments and reactions left in such videos, the music they listen and store, their search and browsing history.

What Facebook and Google do with the big data they collect from their users?

With the big data collected from users, Google and Facebook organize and classify such large volume of data using multiple layers of algorithms. Then, it is analyzed by their systems to create customers’ profiles that help to develop very precise behavioral marketing and advertisement strategies.

Finally, the processed big data is offered to Google and Facebook customers, from retail and consumer brands to governments and politicians, who pay millions of dollars to show products or ideas to audiences who are willing to participate.

Facebook and Google data

Big data 4 v’s applied to digital marketing strategies.

Big data analysis is a valuable business tool that helps companies and managers to improve their operational capacities through a faster and more intelligent decision making process.

However, the main question for marketers is how big data can be used to gain unique advantages over competitors in the world of digital marketing?

Whether it is structured or unstructured, the analysis of large data sets of information can be appalling, slowing simultaneously companies’ decision-making processes. But, when big data is evaluated under the sphere of The Four V’s (volume, variety velocity and veracity), it can help marketers to make smarter decisions.

Four V’s of big data applied to decision-making processes in digital marketing:

Volume: massive amounts of data are created every moment between brands and their followers in social media. When such information is gathered, curated, visualized and shared, it helps to understand important facts about the brand reputation, social media influencers and trending keywords.
Variety: structured and unstructured data comes from multiple sources and is created by machines as well as consumers. For example, structured data represents existing information in databases and unstructured data can be information obtained from hashtags analysis in social media.
Velocity: companies must track web analytics variables such as visits, hits, views and relational marketing information like sales calls and social media interactions in order keep up with the speed of information creation.
Veracity: big data is obtained from diverse sources and multiple devices. cleaning, normalizing and collating those large volumes of data, helps to make truthful and comprehensive decisions.

The way marketers face different challenges associated with how to manage the vast amount of information generated every second in digital media, will determine if big data is useful in improving the decision-making processes.

Structuring a comprehensive analysis of big data sets under the sphere of The Four V’s, will help to create actionable, useful insights that can support marketers’ efforts and business strategies.

https://youtu.be/xJfP_o_fANA

What is the importance of Big Data in public transportation?

The current trend of some companies to use Big Data analytic tools in order to create predictive models of business intelligence is a reality that is impacting the managerial processes in different business environments. However, the fact that the public sector is using more often Big Data analytics to foresee citizens’ needs, change the traditional government management approach that has been in place for decades.

A specific case that elucidates how municipal governments are employing Big Data analytics in their management processes happens in the department of transportation of the city of London, England; TfL, the local government body responsible for the city’s transportation system. This municipal agency has implemented a Big Data analytics strategy that helped to find predictive behaviors of transit users, to identify the needs of the city transportation infrastructure and to improve the agency’s decision making processes.

Similarly, through partnership agreements with different businesses of the city of London, TfL encouraged public transportation users to download a mobile phone application about routes and timetables in their smartphones. The information generated in such app, was intersected with databases that contain information related to routes frequencies in peak hours, routes with greater passenger mobility and annual number of trips per capita using TfL services.

Which aspects of the public transportation in the city of London improved from using big data?

Transportation user experience: After the analysis of large data sets, Tfl could answer questions such as: What is the main purpose of London citizens to use transit systems? What is the preferred mode of transportation among transit users? What is the main source of information used by citizens to obtain information about routes and schedules and What are the main concerns of transit users about the city’s transportation system?

Mobility and sustainability: Using big data, Tfl could forecast important variables such as: transit systems future demand, citizens’ ride time using public transit, transportation network planning and its relationship with the environment (stops, routes and frequency), scope of intermodal transportations systems offered by TfL.

Infrastructure and street furniture: Analyzing large data sets of information, TfL could find out, the impact of new infrastructure in London’s mobility, the use of public space by transit system users and the social and environmental impact of public transportation systems.

Communications: Based on the amount of data generated through the interactions with TfL users, the agency could design strategies to improve the communication among TfL and transit users and to discover what technological platforms were more popular among citizens to communicate with TfL.

The use of Big Data analytic tools by the TfL, helped to bring effective solutions to the needs of public transit users and to identify the most prominent infrastructure and mobility problems of the municipality.

Transportation and big data

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The following article portrays the use of Big Data analytical tools to resolve the needs of public transportation systems in other cities in the world: https://channels.theinnovationenterprise.com/articles/big-data-s-impact-on-public-transportation

Why using visual metaphors improve big data visualization?

The popular adage that says: “a picture is worth a thousand words”, could be considered the statement that best explains the fact that a complex idea can be communicated with greater effectiveness through a graphical representation. Such adage can also be extrapolated to the concept of big data visualization, as it is a communication instrument that helps to illustrate specific volumes of numeric text data in form of visual metaphors using different kinds of charts.

Usually, when communicating large volumes of data represented in numeric text, it becomes difficult for the human brain to symbolize its meaning using the verbal processing function that we employ to interpret daily life phenomena. Therefore, the graphical tools developed to display numerical data sets, allows the human brain to understand information more effectively through visual metaphors.

Likewise, it is pertinent to mention that analogies and metaphors are brain’s cognitive processes that play an essential role in abstract reasoning abilities and human communication. Human brain constantly uses previously acquired knowledge about things we are familiar with, to be aware of unknown phenomena. Thus, graphic metaphors are created in our minds to allow us to understand abstract concepts, such as numeric text data in visual terms.

Additionally, and in order to understand the meaning of a visual metaphor in the analysis of a numerical data set, it is important to remark that the symbols and the graphic resources employed to depict such data must describe numeric values in the most accurate manner. Otherwise, the information authenticity that is intended to be communicated by such data set might be misinterpreted by the final user.

The aforementioned ideas help us to deduce that there is a symbiotic relationship between the types of charts and the sets of numerical data to be analyzed, which influences the way numerical data sets must be presented in the charts and the way users’ perceive data information.

Important questions to ask before designing visual metaphors.

Does the chart aim to compare different values? Are the highest and lowest values of the data set clearly shown in the chart?

Does the chart aim to display the composition of something? Does the chart intents to show independent values as part of a whole set of data?

Does the chart intent to show the distribution of different values? Does the chart help to show statistics outliers, normal trends and the whole range of values expressed in the data set?

Does the chart intent to express the relationship between different groups of data sets? Does the chart show an interrelationship between different variables?

Data visualization greatest strength is that it empowers our brain capacity to process visual information more efficiently than numerical information. Therefore, if a visual metaphor is constructed following a systematic methodology that takes into account data accuracy and graphic coherence; it will communicate charts’ information comprehensively, generating at the same time confidence in the analyzed data by users.

Data visualization